Supplement - Physiographic Variable Raster Data

To link physiographic variables to lithology and trail characteristics (Objective 2), we created process domain maps and used principal component analysis considering physiographic variables such as slope, topographic position index (TPI), and concavity as a function of lithology and trail type. All metrics were calculated using ArcGIS Pro and lidar collected 2013 by McKim & Creed Inc. for City of Boulder. High-resolution lidar data can be useful in determining different types of processes, ranging from slow creep to landslides (Booth et al. 2009; Booth et al. 2013) and variables like slope highlight areas where mass movements are likely to occur. Similarly, concavity, or landscape curvature, indicates where advective versus diffusive processes occur, correlated to convex versus concave landscapes, respectively (Dietrich and Perron 2006; Sweeney et al., 2015). Concave processes are dominated by diffusive movement (slope-dependent transport; Dietrich and Perron 2006). These processes are competing against one another on the hillslope and give rise to diffusion-dominated ridges and advection-dominated valleys (Dietrich and Perron 2006; Sweeney et al., 2015). The TPI is a landform classification used to determine roughness indices like valleys and ridges in the study area. The TPI was calculated at different resolutions (5-m, 10-m, 50-m, 100-m) to see if different hillslope attributes were identifiable at the different scales. To calculate the TPI, the mean for each resolution was subtracted from the Digital Elevation Model (DEM). Derived physiographic variables and GIS data products can be found in this repository.为将地貌变量与岩性及步道特征建立关联（目标2），我们构建了过程域图，并采用主成分分析法，以坡度、地形位置指数（topographic position index, TPI）、凹度等地貌变量作为岩性与步道类型的函数开展分析。所有指标均通过ArcGIS Pro计算，所用激光雷达（lidar）数据由McKim & Creed Inc.于2013年为博尔德市采集。高分辨率激光雷达数据可用于区分不同类型的地表过程，涵盖从缓慢蠕变到滑坡的各类现象（Booth等，2009；Booth等，2013）；坡度等变量可指示易发生块体运动的区域。类似地，凹度（即景观曲率）可指示平流过程与扩散过程的发生位置，分别对应凸状与凹状景观（Dietrich与Perron，2006；Sweeney等，2015）。凹状过程以扩散运动为主（依赖坡度的搬运作用；Dietrich与Perron，2006）。这些过程在山坡上相互竞争，最终形成以扩散作用为主的山脊与以平流作用为主的山谷（Dietrich与Perron，2006；Sweeney等，2015）。地形位置指数（TPI）是一种地貌分类方法，用于确定研究区内河谷、山脊等地貌的粗糙度指标。我们在5m、10m、50m、100m多种分辨率下计算TPI，以探究不同尺度下能否识别出不同的山坡属性。TPI的计算方法为：将数字高程模型（Digital Elevation Model, DEM）的数值减去对应分辨率下的平均值。本存储库中可获取经派生得到的地貌变量与GIS数据产品。